import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
# YOUR CODE HERE
x = stocks.date
y = stocks.GOOG
columns = stocks.iloc[:, 1].count()
fig, ax = plt.subplots()
plt.plot(x,y)
plt.xticks(range(0,columns,14),color='blue',rotation=30)
ax.set_title('Google stock')
ax.set_xlabel('date')
ax.set_ylabel('stock value')
plt.show()
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
# YOUR CODE HERE
#create data
x = stocks.date
y = stocks.GOOG
z = stocks.AAPL
m = stocks.AMZN
n = stocks.FB
p = stocks.NFLX
q = stocks.MSFT
#set lins
columns = stocks.iloc[:, 1].count()
fig, ax = plt.subplots()
plt.plot(x, y, label='GOOG')
plt.plot(x, z, label='AAPL')
plt.plot(x, m, label='AMZN')
plt.plot(x, n, label='FB', linestyle=':')
plt.plot(x, p, label='NFLX', linestyle=':')
plt.plot(x, q, label='MSFT', linestyle=':')
plt.xticks(range(0,columns,14),color='blue',rotation=30)
#set labels
ax.set_title('Google stock')
ax.set_xlabel('date')
ax.set_ylabel('stock value')
plt.legend()
plt.show()
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
# YOUR CODE HERE
print('My question is: is there any relationship between the total bill and tip?')
tips['tip_ratio'] = tips['tip']/tips['total_bill']
sns.jointplot(x='tip', y='tip_ratio', data=tips)
plt.show()
My question is: is there any relationship between the total bill and tip?
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
# YOUR CODE HERE
#stocks = px.data.stocks()
#fig = px.line(stocks, x="date", y=stocks.columns, markers=True)
#fig.show()
# YOUR CODE HERE
stocks = px.data.stocks()
names = list(set(stocks.columns))
names = names[1:]
col = ['date', 'value', 'name']
new_data = pd.DataFrame(columns = col)
t = 0
for i in range(len(stocks.index)):
for j in range(6):
new_data.loc[t] = [stocks.iloc[i, 0], stocks.iloc[i, j + 1], names[j]]
t += 1
fig = px.line(new_data, x = 'date', y = 'value', color = 'name', symbol = 'name')
fig.show()
# YOUR CODE HERE
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color="sex", facet_col="smoker", facet_row="time")
fig.show()
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
# YOUR CODE HERE
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
fig = px.bar(df_2007_new, x="pop", y=df_2007_new.index, color=df_2007_new.index, orientation='h', text_auto= True)
fig = fig.update_yaxes(categoryorder = 'total ascending')
fig.show()